Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
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Recently, Nogueira et al.  proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task.
We consider algorithm selection in the context of ad-hoc information retrieval.
In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods.
This paper presents an LDA-based model that generates topically coherent segments within documents by jointly segmenting documents and assigning topics to their words.
With this in mind, we argue that embedding KBs within deep neural architectures supporting documentquery matching would give rise to fine-grained latent representations of both words and their semantic relations.